As a Spark Technical Solutions Engineer, you will provide technical and consulting related solutions for the challenging Spark/ML/AI/Delta/Streaming/Lakehouse reported issues by our customers and resolve any challenges involving the Databricks unified analytics platform with your comprehensive technical and client-facing skills. You will assist our customers in their Databricks journey and provide them with the guidance and expertise that they need to accomplish value and achieve their strategic goals using our products.
The impact you will have:
- Performing initial level analysis and troubleshooting issues in Spark using Spark UI metrics, DAG, Event Logs for multiple customer reported job slowness issues.
- Troubleshoot, resolve and suggest deep code-level analysis of Spark to address customer issues related to Spark core internals, Spark SQL, Structured Streaming, Delta, Lakehouse and other databricks runtime features.
- Assist the customers in setting up reproducible spark problems with solutions in the areas of Spark SQL, Delta, Memory Management, Performance tuning, Streaming, Data Science, Data Integration areas in Spark.
- Participate in the Designated Solutions Engineer program and guide one or two of strategic customer's daily Spark and Cloud issues.
- Coordinate with Account Executives, Customer Success Engineers and Resident Solution Architects for coordinating the customer issues and best practices guidelines.
- Participate in screen sharing meetings, answering slack channel conversations with our team members and customers, helping in driving the major spark issues at an individual contributor level.
- Build an internal wiki, knowledge base with technical documentation, manuals for the support team and for the customers. Help create company documentation and knowledge base articles.
- Coordinate with Engineering and Backline Support teams to help report product defects.
- Participate in weekend and weekday on-call rotation and run escalations during databricks runtime outages, incident situations, and plan day 2 day activities and provide escalated level of support for important customer operational issues.
What we look for:
- 3 years of hands-on experience developing any two or more of the Big Data, Hadoop, Spark,Machine Learning, Artificial Intelligence, Streaming, Kafka, Data Science, ElasticSearch related industry use cases at the production scale. Spark experience is mandatory.
- Experience in the performance tuning/troubleshooting of Hive and Spark-based applications at production scale.
- Real-time experience in JVM and Memory Management techniques such as Garbage collections, Heap/Thread Dump Analysis.
- Experience with any SQL-based databases, Data Warehousing/ETL technologies like Informatica, DataStage, Oracle, Teradata, SQL Server, MySQL and SCD type use cases.
- Experience with AWS or Azure or GCP
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
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